Add benchmarks for machine learning application

Today, Machine learning applications are important use-cases of Python.
I haven't prepared concrete benchmark implementations yet, but I would like to suggest guidelines for machine learning benchmarks.

A. Each benchmark should provide all of the following implementations and shows the same result.

• Pure Python-based implementation (might be not easy). Sympy based implementation
• Numpy-based implementation.
• (optional) Famous frameworks like scikit-learn, TensorFlow, or PyTorch-based implementation.

B. Following algorithm-based benchmark should provide training and inference benchmark.

• Regression algorithm
• Decision tree algorithm
• Clustering algorithm
• Nearest neighborhood algorithm
• Matrix factorization
• ... (Please suggest!)

C. Deep learning-based or neural network-based benchmarks only provide inference benchmark with fixed weights since training benchmark needs GPU resources but using GPU resource is out of the topic.

• Simple neural network
• ... (Please suggest!)

L’issue ne nomme aucun fichier, test ou point d’entrée et ne fournit pas d’implémentations concrètes de benchmarks. Commencez par examiner les conventions de benchmark existantes du dépôt, puis clarifiez les algorithmes et les implémentations concernés ; pour considérer la tâche comme terminée, il faudrait convenir d’un ensemble de benchmarks d’entraînement et d’inférence correspondants.